Methods, systems, articles of manufacture and apparatus to improve boundary excursion detection

ABSTRACT

Methods, apparatus, systems and articles of manufacture are disclosed to improve boundary excursion detection. An example apparatus to improve boundary excursion detection includes a metadata extractor to parse a first control stream to extract embedded metadata, a metadata label resolver to classify a boundary term of the extracted embedded metadata, a candidate stream selector to identify candidate second control streams that include a boundary term that matches the classified boundary term of the first control stream, and a boundary vector calculator to improve boundary excursion detection by calculating a boundary vector factor based on respective ones of the candidate second control streams that include the classified boundary term.

FIELD OF THE DISCLOSURE

This disclosure relates generally to control systems, and, moreparticularly, to methods, systems, articles of manufacture and apparatusto improve boundary excursion detection.

BACKGROUND

In recent years, manufacturing and industrial environments have becomemore automated. Such environments include industrial equipment that iscontrolled by one or more control systems (e.g., process controlsystems). The equipment may include conveyor belts, chemical tanks, airand/or fluid pumps, heating equipment, welding equipment, robotics,injection mold equipment, etc. Additionally, the equipment typically hasdifferent types of configuration settings depending on the objective ofthe manufacturing environment. As such, the same type of equipment maybe used in a first manufacturing environment with first settings, andthat same type of equipment may be used in a second manufacturingenvironment with second settings that are different than the first.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic illustration of an example control environmentincluding an example boundary analyzer constructed in accordance withthe teachings of this disclosure to improve boundary excursiondetection.

FIG. 2 is an example stream table generated by the example boundaryanalyzer.

FIG. 3 is an example calculation table generated by the example boundaryanalyzer.

FIGS. 4-7 are flowcharts representative of machine readable instructionswhich may be executed to implement the example boundary analyzer of FIG.1 to improve boundary excursion detection.

FIG. 8 is a block diagram of an example processing platform structuredto execute the instructions of FIGS. 4-7 to implement the exampleboundary analyzer of FIG. 1 to improve boundary excursion detection.

The figures are not to scale. Instead, the thickness of the layers orregions may be enlarged in the drawings. In general, the same referencenumbers will be used throughout the drawing(s) and accompanying writtendescription to refer to the same or like parts.

DETAILED DESCRIPTION

As used herein, “factory environments,” “manufacturing environments,” or“environments” include facilities that produce or augment one or moreproducts/components (e.g., electronics, machines, chemicals, vehicles,etc.) using industrial equipment. Such nomenclature also refers tolaboratory facilities/environments (e.g., facilities that facilitateproduct testing, chemical testing, etc.). The environments utilize manydifferent types of equipment that is controlled by control systems(e.g., including but not limited to process control systems). Theequipment may include conveyor belts, chemical tanks, air and/or fluidpumps, heating equipment, welding equipment, robotics, injection moldequipment, etc.

One environment may include any number of “lines,” each of which isdedicated to a particular product to be manufactured. While the sametype of equipment may exist on each manufacturing line, configurationsettings for that equipment may differ. For example, a firstmanufacturing line may build a first product, or assembly of productsthat require a tank to supply high-pressure saline spray at a relativelyhigh temperature (e.g., a pre-annealing process), while a secondmanufacturing line may build a second product in which the same type oftank supplies low-pressure rinse water at a relatively low temperature.As such, one or more environment personnel (e.g., process engineers,control engineers, process control personnel, factory line workers,etc.) are typically chartered with the responsibility of configuringoperating limits for each piece of automated equipment on each line in amanner that (a) results in a product having acceptable quality standardsand (b) ensures the safety of the environment.

The factory environments tend to be tightly controlled with a samecontrol process running for an extended period of time. As disclosedabove, a first product will require the assembly line to be configured,tuned and/or otherwise calibrated until a satisfactory configuration isidentified. Once the satisfactory configuration is identified, thatconfiguration (e.g., equipment operating limits, boundaries, etc.) tendsto be used without further modification to allow any number of productsto be manufactured. However, manufacturing environments are susceptibleto control anomalies that require one or more adjustments to equipmentsetpoints. As used herein, boundaries (sometimes referred to asparameters) include a boundary name and a corresponding boundary value.The boundary names are indicative of equipment names or equipment outputvalue names, while corresponding boundary values refer to equipmentoperating parameters (e.g., equipment setpoints), which havecorresponding values (sometimes referred to as boundary values orparameter values) that are set to achieve a manufacturing objectiveand/or ensure operating safety. For example, a tank boundary may includea high pressure (e.g., maximum) operating limit to prevent excessivestress on the tank, ensure a satisfactory result on a manufactured part,and ensure safety of nearby factory personnel. While the tank boundaryvalue may be configured by the manufacturing engineer during the monthof February, environmental changes may occur due to weather variationsduring the relatively warmer months of July and August. Similarly,mechanical changes may occur due to wear, and electrical changes mayoccur due to demands from adjacent manufacturing activities (e.g.,voltage fluctuations in view of adjacent equipment starts/stops, inwhich the adjacent equipment shares common powerline supplies).Accordingly, a previously calibrated tank boundary value (e.g., apressure value) set in February may be exceeded (also referred to as anexcursion) during the month of July (due to elevated environmentaltemperatures) and cause one or more abatement procedures to be invoked(e.g., sounding an alarm, opening a pressure release valve, turning offa heater, etc.).

While a boundary value excursion may occur, control personnel (e.g.,process control personnel) may ignore, dismiss and/or otherwise cancelthe abatement procedures because the excursion is not of a type (e.g., amagnitude) that is problematic. The boundary value excursion may becaused by errors associated with human discretion, such as processcontrol personnel establishing, configuring and/or otherwise calibratingboundary values based on assumptions, heuristics and/or guesses. Suchhuman discretion introduces one or more risks to product quality,assembly line efficiency and/or safety (e.g., human safety, equipmentsafety). When control personnel ignore, dismiss or otherwise silence anexcursion, an efficiency metric of the assembly line is reduced,particularly when the excursion halts the assembly line process untilauthorized personnel can reset the excursion. In some examples, theprocess control personnel have the authorization to silence and/orotherwise override one or more corrective actions that result from theexcursion, but such personnel may not have the authorization to adjustthe problematic boundary value. In some examples, the process controlpersonnel override the excursion event because equipment operatingconditions are within proper specification limits, but such boundaryvalues were incorrectly established due to human discretion orguesswork.

While many manufacturing settings operate in connection with expectedboundary values measured in data streams that were established by humandiscretionary choices, examples disclosed herein improve boundaryexcursion detection by adjusting boundary values based on empiricaldata. In other words, examples disclosed herein improve boundaryexcursion detection by allowing boundary values (e.g., equipmentsetpoints) to be set in a more accurate manner such that if they aresatisfied (e.g., exceeded), then the likelihood of a valid excursion ispresent. Additionally, examples disclosed herein establish boundaryvalues based on similarity metrics derived from empirical data acquiredfrom data streams of other process control environments (e.g., vettedstreams considered to be operating with appropriately set boundaryvalues). As used herein, streams (sometimes referred to herein asprocess control streams) refer to a set of process/operating dataincluding any number of boundaries (e.g., boundary names (terms) andcorresponding boundary values) having associated values frommanufacturing equipment (e.g., sensor data) operating in a factoryenvironment. As described in further detail below, streams may becharacterized with one or more boundary vector(s) indicative of a set ofboundary values that have been observed (e.g., a numerical valueindicative of a signature, a maximum/minimum data pair, etc.). Examplesdisclosed herein solve problems associated with certain boundary values(e.g., threshold setpoint values that trigger alarms and/or safetyprotocols) that are set too conservatively and cause nuisance warnings,which lead to process inefficiency and/or process downtime.Additionally, examples disclosed herein solve problems associated withcertain boundary values that are set in connection with human discretionand/or guesswork.

FIG. 1 is a schematic illustration of an example control environment100. In the illustrated example of FIG. 1, the control environment 100(e.g., a process control environment) includes any number of controlsystems 102 (e.g., process control systems), which may be geographicallyseparated (e.g., a first process control system in a first building, asecond process control system in a second building, a third processcontrol system in a separate state/country, etc.). The example controlsystems 102 are communicatively connected via an example network 104 toan example boundary analyzer 106. In some examples, the boundaryanalyzer 106 is connected to the example control systems 102 via one ormore other communication techniques/technologies, including, but notlimited to a General Purpose Interface Bus (GPIB) (IEEE 488). Theexample boundary analyzer 106 includes an example data retriever 108, anexample metadata extractor 110, and an example labeler 112communicatively connected to an example label association storage 114.While the example label association storage 114 is shown in theillustrated example of FIG. 1 as directly connected to the examplelabeler 112, communication therebetween may occur in some examples viaany other communication standard and/or technique (e.g., via the examplenetwork 104). Generally speaking, one or more components and/or datasources/storage of the illustrated example of FIG. 1 may be facilitatedvia direct connection and/or network connection(s).

The example boundary analyzer 106 of FIG. 1 also includes an examplestream comparer 116, which includes an example candidate stream selector118 and an example metadata label resolver 120 communicatively connectedto the example label association storage 114, and an example streamprofile storage 138. The example boundary analyzer 106 of FIG. 1 alsoincludes an example stream weighting engine 122, which includes anexample difference calculator 124, an example similarity metriccalculator 126, and an example boundary vector calculator 128, which iscommunicatively connected to an example control profile storage 140. Theexample boundary analyzer 106 of FIG. 1 also includes an example controlconfiguration manager 150, an example environment detail extractor 130,an example limit tester 132, an example override detector 134, and anexample artificial intelligence (AI) engine 136. One or more of thecomponents and/or storage (e.g., storage devices, databases, etc.) ofthe illustrated example of FIG. 1 are communicatively connected via oneor more communication busses 142.

In operation, the example data retriever 108 retrieves a stream, such asa process control stream from a manufacturing process from one of theexample control systems 102. The example metadata extractor 110 parsesthe retrieved stream for process data and extracts associated metadata(e.g., metadata embedded within the retrieved stream—“embeddedmetadata”) associated therewith. The example metadata extractor 110 ishardware that may be implemented as a means for parsing or a parsingmeans, in which the means for parsing and/or parsing means are hardware.In some examples, the metadata extractor 110 generates a table ofextracted metadata (e.g., extracted embedded metadata). Metadata mayinclude, but is not limited to alphanumeric data within a string ofsetpoint values indicative of process details, such as names of sensors,names of setpoints, equipment manufacturer serial numbers/model numbers,etc. FIG. 2 illustrates an example stream table 200 including examplemetadata extracted by the example metadata extractor 110. In theillustrated example of FIG. 2, the stream table 200 includes a retrievedstream boundary name column 202 that includes metadata boundary names,and a retrieved stream boundary value column 204 that includescorresponding values associated with metadata boundary names. In theretrieved stream boundary name column 202 of FIG. 2, boundary namesinclude an example high-temperature boundary name 206 (“TEMP-HIGH”) anda corresponding boundary value 208 (“127° F.”). In some examples, theretrieved stream boundary name column 202 includes informationindicative of a type of sensor or equipment, such as an exampleequipment type 210 (“Tank”) and a corresponding value 212(“Manchester”). The illustrated example of FIG. 2 also includes aprocess name 214 and an associated value 216 (“Air Tool Supply”)indicative of a type of manufacturing activity. Other columns of theexample stream table 200 of FIG. 2 will be discussed in further detailbelow.

The example metadata extractor 110 determines whether the retrievedstream is associated with a new process that was not previously analyzedby the example boundary analyzer 106. For example, the retrieved streammay include a boundary name in the retrieved stream boundary name column202 entitled “New Stream” with an associated binary value (e.g., “Yes,”“No,” “True,” “False,” “0,” “1,” etc.). In the event that the retrievedstream is not associated with a new and/or otherwise unknown process(e.g., a binary value of “False”), further label resolution efforts maybe bypassed. Generally speaking, boundary names of retrieved metadataare sourced from any number of independent sensor and/or equipmentmanufacturers that use unique nomenclature to identify one or moreboundaries. For instance, a first manufacturer may refer to atemperature boundary as “TEMP,” while a second manufacturer may refer tothe temperature boundary as “Temperature.” In the event that theretrieved stream is associated with a new and/or otherwise unknownprocess (e.g., a binary value of “True”), the example labeler 112applies one or more nomenclature resolution techniques, such as NaturalLanguage Processing (NLP) techniques and/or nomenclature lookup tableinformation stored in the example label association storage 114. Assuch, any retrieved stream may be normalized to resolve uniquenomenclature that is indicative of known boundary types. In someexamples, such nomenclature resolution may be performed at a later stageof stream analysis, as described in further detail below.

The example stream comparer 116 compares the retrieved stream, sometimesreferred to herein as the “current stream” to any number of streamprofiles stored in the example stream profile storage 138. The examplemetadata extractor 110 selects a metadata boundary from the retrievedstream and the example candidate stream selector 118 identifies one ormore candidate stream profiles that might be similar. For example, ifthe retrieved stream includes a boundary name “TEMP-HIGH,” then theexample candidate stream selector 118 flags only those previously storedstreams in the example stream profile storage 138 that also include asimilarly-named boundary. Because the retrieved stream may have anynumber of individual and/or otherwise unique boundary names, the examplecandidate stream selector 118 determines whether one or moreadditional/alternate boundary names are in the retrieved stream thathave not yet been compared to profile streams stored in the examplestream profile storage 138. The example candidate stream selector 118 ishardware that may be implemented as a means for identifying or anidentifying means, in which the means for identifying and/or identifyingmeans are hardware.

After the example candidate stream selector 118 identifies one or morecandidate streams that have some amount/degree (e.g., a magnitude ofsimilarity) of similarity to the retrieved stream (e.g., by virtue of atleast one common boundary term/name), the example metadata labelresolver 120 classifies boundary terms by applying a similaritycalculation between boundary names. The example metadata label resolver120 is hardware that may be implemented as a means for classifying or aclassifying means, in which the means for classifying and/or classifyingmeans are hardware. In some examples, the metadata label resolver 120employs Natural Language Processing (NLP) techniques to determinewhether a boundary name has one or more likely equivalents. Forinstance, a boundary name “TEMP” may be equated and/or otherwise matchedto any variant related to a “temperature” boundary. In some examples,the metadata label resolver 120 employs lookup tables in addition to orinstead of the example NLP techniques. When candidate streams having adegree of similarity are identified and boundary nomenclature isresolved and/or otherwise normalized, the example metadata labelresolver 120 stores the matching streams for further analysis, such asstoring the candidate matching streams to the example stream table 200of FIG. 2.

Returning to the illustrated stream table 200 of FIG. 2, stream S₄ (220)is added by the example metadata label resolver 120 or the examplecandidate stream selector 118. The example stream S₄ (220) is selectedbecause at least one of its boundaries relates to the high-temperatureboundary 206, a low-temperature boundary 222, a high-pressure boundary224, a low-pressure boundary 226, the process name 214, or the exampleequipment type 210. Similarly, the example metadata label resolver 120or the example candidate stream selector 118 adds stream S₇ (228) to theexample stream table 200 of FIG. 2 for having at least one matchingboundary name.

Although two or more streams may have at least one common boundary name,a degree of similarity between such streams is determined by more thanjust a singular similar boundary name. The example candidate streamselector 118 selects one of the previously identified candidate streamsthat have at least one matching boundary name. Within the currentstream, there may be one or more other matching boundary names havingcorresponding boundary values. As described above, the example streamtable of FIG. 2 includes stream S₄ (220) and stream S₇ (228) that haveat least one similar boundary name to the retrieved stream 202.Additionally, the example difference calculator 124 generatescorresponding delta columns to identify a percent difference between oneor more values of common boundary names. In the illustrated example ofFIG. 2, the difference calculator 124 generates a delta-S₄ column 230and a delta-S₇ column 232. Additionally, the example differencecalculator 124 calculates an S₄ temp-high difference value 234 (e.g.,2.3%) that is based on a deviation between the TEMP-HIGH value 208 and aTEMP-HIGH value 236 associated with stream S₄ 220. Similarly, theexample difference calculator 124 calculates an S₇ temp-high differencevalue 238 (e.g., a value difference of 22 degrees divided by averagevalue of 116 degrees times 100=18.9%) that is based on a deviationbetween the TEMP-HIGH value 208 of the retrieved stream 204 and aTEMP-HIGH value 240 associated with stream S₇ 228. As described infurther detail below, the relatively greater difference value in streamS₇ as compared to the difference value in stream S₄ for the sameboundary name suggests that stream S₄ is more similar to the retrievedstream than S₇. The example difference calculator 124 is hardware thatmay be implemented as a means for calculating, a means for differencecalculating, a difference calculating means or a calculating means, inwhich such means are hardware.

The example similarity metric calculator 126 calculates a streamsimilarity metric based on an aggregate of percent difference values forboundary values. In the illustrated example of FIG. 2, the examplesimilarity metric calculator 126 calculates stream similarity metric ina manner consistent with example Equation 1.

$\begin{matrix}{S_{XN} = {100 - {\frac{\Sigma \left( {\% \mspace{14mu} {difference}\mspace{14mu} {values}} \right)}{\# \mspace{14mu} {of}\mspace{14mu} {matching}\mspace{14mu} {parameters}}.}}} & {{Equation}\mspace{14mu} 1}\end{matrix}$

In the illustrated example of Equation 1, S_(XN) represents a similaritymetric between stream X (e.g., a previously stored profile stream) andretrieved stream N (e.g., the current/new stream under analysis).Generally speaking, the similarity metric is indicative of a magnitudeof similarity between streams. The example similarity metric calculator126 calculates stream similarity metric S_(4N) (242) having an examplevalue of 0.912 based on the average percent difference values betweenthe retrieved stream (N) and stream S₄ (e.g., the sum of values 2.3%,2.5%, 28% and 2.5% divided by four matching boundaries between theretrieved stream and stream S₄). The example similarity metriccalculator 126 also calculates stream similarity metric S_(7N) (244)having an example value of 0.6865 based on the average percentdifference values between the retrieved stream (N) and stream S₇ (e.g.,the sum of values 18.9%, 78.5%, 0% and 28% divided by four matchingboundaries between the new stream and stream S₇). Generally speaking,the relative differences between stream similarity metric S_(4N) (242)(0.912) and similarity metric S_(7N) (244) (0.6865) indicate that streamS₄ is more similar (because it is closer to unity) to the retrievedstream than stream S₇. As such, examples disclosed herein utilizecharacteristics of the more similar stream to a greater extent (e.g.,weight more heavily) when modifying the retrieved stream, which isparticularly helpful when the retrieved stream values/boundaries mayhave been set based on mere discretionary guesswork or heuristics. Theexample similarity metric calculator 126 is hardware that may beimplemented as a means for calculating, a means for similarity metriccalculating, a similarity metric calculating means, or a calculatingmeans, in which such means are hardware.

The example boundary vector calculator 128 calculates a boundary vectorfactor (sometimes referred to as a “boundary vector,” or a “factor”) forthe retrieved stream, in which the calculated factor adjusts theboundary values in a manner that better aligns with expected operatingsetpoints that will reduce nuisance alarms (e.g., nuisance boundaryexcursions), improve process efficiency and improve process safety. Asdescribed above, the retrieved stream (e.g., a stream of boundarysetpoints for equipment from a process control system) may not be setand/or otherwise established correctly. In some examples, boundaryvalues are equipment setpoints (e.g., maximum operating values, minimumoperating values, etc.) that have not been configured in connection withempirical data or manufacturer-suggested guidance. In suchcircumstances, boundary values may be set in a manner that degradesprocess efficiency and/or elevates one or more safety risks associatedwith the equipment, process control system and/or factory environment.The example boundary vector calculator 128 is hardware that may beimplemented as a means for calculating or a calculating means, in whichthe means for calculating and/or calculating means are hardware.

In some examples, the example boundary vector calculator 128 calculatesthe boundary vector factor based on the (relatively) most similar streamof the example stream table 200. As described above, stream S₄ exhibitsthe closest relative similarity to the retrieved stream because it hasthe largest relative similarity metric (0.912) (e.g., because itexhibits the relative lowest aggregate difference between matchingboundary values). However, in some examples the boundary vectorcalculator 128 calculates the boundary vector factor in a mannerconsistent with example Equation 2.

$\begin{matrix}{B_{n} = {\sum\limits_{x = 1}^{n - 1}\; {S_{xn}{B_{x}.}}}} & {{Equation}\mspace{14mu} 2}\end{matrix}$

In the illustrated example of Equation 2, Bn represents a boundaryvector for stream n (e.g., the retrieved stream of interest), Sxnrepresents a similarity metric between stream x and retrieved stream n,and Bx represents a boundary vector of stream x. Generally speaking,some streams and/or stream data stored in the example stream profilestorage 138 include a boundary vector Bx, which may be indicative of aparticular equipment setpoint value. However, in some examples, theboundary vector calculator 128 calculates the boundary vector factor ina manner consistent with example Equation 3.

$\begin{matrix}{B_{n} = {\left( \frac{\Sigma_{x = 1}^{n - 1}S_{xn}B_{x}}{n} \right) \div 100.}} & {{Equation}\mspace{14mu} 3}\end{matrix}$

In the illustrated example of Equation 3, the boundary vector Bn for thestream is based on an aggregate consideration of boundary values of theretrieved stream and the candidate stream of interest x, as shown inFIG. 3. Additionally, the illustrated examples of Equation 2 andEquation 3 calculate boundary vector values in a manner proportionate toa similarity metric.

In the illustrated example of FIG. 3, the boundary vector calculatorgenerates a calculation table 300 having a candidate stream boundarycolumn 302 (stream x values, such as 1_(TH) indicative of the firstcandidate stream temperature-high boundary, 1_(TL) indicative of thefirst candidate stream temperature-low boundary, 2_(PH) indicative ofthe second candidate stream pressure-high boundary, etc.), a similaritymetric column 304 having corresponding similarity metric values (Sxn), astream-x boundary vector column 306 having corresponding boundary vectorvalues Bx, and a stream-n boundary vector column 308 havingcorresponding boundary vector values for the retrieved stream n. In amanner consistent with example Equation 3, a first example row 310calculates a Bn vector value corresponding to the example firstcandidate stream temperature-high boundary (1_(TH)) and, as describedabove, a corresponding similarity metric value of 0.912 was calculatedin view of candidate stream x and retrieved stream n. As shown in theillustrated example of Equation 3, Bn vector values are calculated in amanner that is proportionate to a degree or quantity of similaritybetween the retrieved stream value(s) and candidate stream value(s). Assuch, the example boundary vector calculator 128 calculates a Bn vectorvalue for the boundary associated with a high temperature as 118.56degrees (312).

The example boundary vector calculator 128 repeats the aforementionedcalculation in a manner consistent with example Equation 3 such that allcommon boundaries (and their corresponding values) between the retrievedstream and the candidate stream (x) are considered. These correspondingvalues are averaged and divided by 100 to generate the boundary vectorfactor, which is shown with example data of FIG. 3 as 0.9586 (314). Theexample control configuration manager 150 applies this boundary vectorfactor to respective boundary values of the retrieved stream as anadjustment factor (a boundary vector factor) in an effort to tailorsetpoint values that improve process efficiency, safety and/or otherwisetailor such setpoint values using the benefit of empirical behaviors ofpreviously stored streams in the example stream profile storage 138. Inparticular, the example control configuration manager 150 generates anupdated stream (e.g., an updated process control stream) by applying theboundary vector factor to boundary values. Updated boundary values,thus, reduce a frequency of nuisance boundary excursion instances.Additionally, because new and/or current streams are iterativelyanalyzed by examples disclosed herein, continuous improvement ofboundary values are realized, particularly in view of potentiallychanging environmental conditions (e.g., seasonal weather changes,equipment wear, etc.). The example control configuration manager 150 ishardware that may be implemented as a means for configuration, or aconfiguration means, in which such means are hardware.

While the aforementioned activity considers dynamic tailoring and/ormodification of boundary values in connection with empirical stream dataassociated with streams similar to the retrieved stream of interest,examples disclosed herein also consider the effects of runtimebehaviors. For example, during runtime of a control system one or moreboundary values may be satisfied (e.g., exceeded) causing alarm and/orcontrol change activity (e.g., stopping a process, slowing a process,activating safety shields, etc.). However, in the event that suchboundary values were not initially set correctly, then control personnelmay dismiss such alarms and/or controls as a nuisance. The nuisance mayhave a detrimental effect on control efficiency that examples disclosedherein overcome.

The example limit tester 132 determines whether a boundary value isexceeded and, if so, initiates corrective activity. As described above,corrective activity may be programmed by control systems to enactwarnings, alarms and/or safety systems. After such corrective activityis performed, the example override detector 134 captures responseactivity (post excursion inputs) of the boundary value violation.Response activity includes override actions taken by process controlpersonnel, such as silencing an alarm and/or restarting processactivity. Response activity also includes acquiesce actions taken byprocess control personnel, such as acknowledgement activities andrequests for corrective action and/or repair (e.g., requests formaintenance, observing threshold durations of sustained downtime afterthe boundary value violation, etc.). The example override detector 134detects one or more post excursion inputs, such as alarm acceptanceinstructions, shut-down instructions, maintenance request instructions,override instruction(s), process shut-down instruction(s), etc. Theexample override detector 134 is hardware that may be implemented as ameans for detecting, a means for override detecting, or an overridedetecting means, in which such means are hardware.

Generally speaking, examples disclosed herein capture and analyzeresponse activity because it reveals useful information related towhether boundary values have been configured correctly. For example,repeated operator override behaviors of boundary value violations(excursions) indicate that such boundary values are not configuredcorrectly. In some examples, process control personnel are aware ofreasonable operating limits of process control equipment that may nothave been considered by other personnel that originally established suchboundary values. As such, the example override detector 134 feeds and/orotherwise forwards response activity to the example artificialintelligence engine 136. Based on one or more iterations of responseactivity (e.g., override, acquiesce, ignore, etc.), the exampleartificial intelligence engine 136 updates existing boundary values ofthe new/current stream in view of one or more detected patterns, inwhich such modified boundary values (e.g., candidate boundary valueadjustments) may be saved in the example control profile storage 140.

Because the retrieved stream may be relatively new in a factoryenvironment and/or process control system, the example stream comparer116 determines whether the retrieved stream just analyzed has performeda threshold number of iterations. If not, then the retrieved stream isnot flagged for distribution as a candidate stream to be used forsimilarity comparison purposes. Stated differently, the retrieved streamhas not yet been vetted by examples disclosed herein until a thresholdnumber of comparisons, adjustments and/or artificial intelligencepattern recognition instances have occurred. During each such iteration,boundary values are adjusted accordingly and a convergence occurs wheremagnitudes of each adjustment become smaller and smaller. On the otherhand, when a threshold number of iterations of the retrieved stream haveoccurred, the example stream comparer 116 flags the stream and/or thecorresponding boundary values for distribution, which can be used tohelp tailor and/or otherwise configure other/future new streams andcorresponding boundary values.

Examples disclosed herein also assist control personnel when initiallyconfiguring a new manufacturing and/or other control environment thathas not previously been established. For instance, to combat problemsassociated with control personnel establishing boundary values absentempirical basis, examples disclosed herein provide starting-pointboundary values based on similar control systems that have previouslybeen established and contain similar control equipment and/or controlobjectives. The example control configuration manager 150 determineswhether a new environment is to be configured and, if so, the exampleenvironment detail extractor 130 retrieves environment details.Environment details include, but are not limited to a list of controlequipment, manufacturer names of the control equipment, model/serialnumbers of the control equipment, nomenclature designating an intendedpurpose of the control equipment (e.g., air tool supply, condensatecollector, etc.), etc. The example environment detail extractor 130selects a match between the environment detail and one or more controlprofiles stored in the example control profile storage 140 that havepreviously been flagged for distribution.

Boundary values from matching control profiles are used by the examplecontrol configuration manager 150 to configure the new control systemand/or equipment therein with boundary values. The example controlconfiguration manager 150 then enables a runtime mode for the newlyestablished control system.

While an example manner of implementing the example control environment100 of FIG. 1 is illustrated in FIGS. 1-3, one or more of the elements,processes and/or devices illustrated in FIGS. 1-3 may be combined,divided, re-arranged, omitted, eliminated and/or implemented in anyother way. Further, the example data retriever 108, the example metadataextractor 110, the example labeler, the example stream comparer 116, theexample candidate stream selector 118, the example metadata labelresolver 120, the example stream weighting engine 122, the exampledifference calculator 124, the example similarity metric calculator 126,the example boundary vector calculator 128, the example controlconfiguration manager 150, the example environment detail extractor 130,the example limit tester 132, the example override detector 134, theexample artificial intelligence engine 136 and/or, more generally, theexample boundary analyzer 106 of FIG. 1 may be implemented by hardware,software, firmware and/or any combination of hardware, software and/orfirmware. Thus, for example, any of the example data retriever 108, theexample metadata extractor 110, the example labeler, the example streamcomparer 116, the example candidate stream selector 118, the examplemetadata label resolver 120, the example stream weighting engine 122,the example difference calculator 124, the example similarity metriccalculator 126, the example boundary vector calculator 128, the examplecontrol configuration manager 150, the example environment detailextractor 130, the example limit tester 132, the example overridedetector 134, the example artificial intelligence engine 136 and/or,more generally, the example boundary analyzer 106 of FIG. 1 could beimplemented by one or more analog or digital circuit(s), logic circuits,programmable processor(s), programmable controller(s), graphicsprocessing unit(s) (GPU(s)), digital signal processor(s) (DSP(s)),application specific integrated circuit(s) (ASIC(s)), programmable logicdevice(s) (PLD(s)) and/or field programmable logic device(s) (FPLD(s)).When reading any of the apparatus or system claims of this patent tocover a purely software and/or firmware implementation, at least one ofthe example data retriever 108, the example metadata extractor 110, theexample labeler, the example stream comparer 116, the example candidatestream selector 118, the example metadata label resolver 120, theexample stream weighting engine 122, the example difference calculator124, the example similarity metric calculator 126, the example boundaryvector calculator 128, the example control configuration manager 150,the example environment detail extractor 130, the example limit tester132, the example override detector 134, the example artificialintelligence engine 136 and/or, more generally, the example boundaryanalyzer 106 of FIG. 1 is/are hereby expressly defined to include anon-transitory computer readable storage device or storage disk such asa memory, a digital versatile disk (DVD), a compact disk (CD), a Blu-raydisk, etc. including the software and/or firmware. Further still, theexample boundary analyzer 106 of FIG. 1 may include one or moreelements, processes and/or devices in addition to, or instead of, thoseillustrated in FIGS. 1-3, and/or may include more than one of any or allof the illustrated elements, processes and devices. As used herein, thephrase “in communication,” including variations thereof, encompassesdirect communication and/or indirect communication through one or moreintermediary components, and does not require direct physical (e.g.,wired) communication and/or constant communication, but ratheradditionally includes selective communication at periodic intervals,scheduled intervals, aperiodic intervals, and/or one-time events.

Flowcharts representative of example hardware logic, machine readableinstructions, hardware implemented state machines, and/or anycombination thereof for implementing the boundary analyzer 106 of FIG. 1is shown in FIGS. 4-7. The machine readable instructions may be anexecutable program or portion of an executable program for execution bya computer processor such as the processor 812 shown in the exampleprocessor platform 800 discussed below in connection with FIG. 8. Theprogram(s) may be embodied in software stored on a non-transitorycomputer readable storage medium such as a CD-ROM, a floppy disk, a harddrive, a DVD, a Blu-ray disk, or a memory associated with the processor812, but the entire program and/or parts thereof could alternatively beexecuted by a device other than the processor 812 and/or embodied infirmware or dedicated hardware. Further, although the example program(s)is/are described with reference to the flowcharts illustrated in FIGS.4-7, many other methods of implementing the example boundary analyzer106 may alternatively be used. For example, the order of execution ofthe blocks may be changed, and/or some of the blocks described may bechanged, eliminated, or combined. Additionally or alternatively, any orall of the blocks may be implemented by one or more hardware circuits(e.g., discrete and/or integrated analog and/or digital circuitry, anFPGA, an ASIC, a comparator, an operational-amplifier (op-amp), a logiccircuit, etc.) structured to perform the corresponding operation withoutexecuting software or firmware.

As mentioned above, the example processes of FIGS. 4-7 may beimplemented using executable instructions (e.g., computer and/or machinereadable instructions) stored on a non-transitory computer and/ormachine readable medium such as a hard disk drive, a flash memory, aread-only memory, a compact disk, a digital versatile disk, a cache, arandom-access memory and/or any other storage device or storage disk inwhich information is stored for any duration (e.g., for extended timeperiods, permanently, for brief instances, for temporarily buffering,and/or for caching of the information). As used herein, the termnon-transitory computer readable medium is expressly defined to includeany type of computer readable storage device and/or storage disk and toexclude propagating signals and to exclude transmission media.

“Including” and “comprising” (and all forms and tenses thereof) are usedherein to be open ended terms. Thus, whenever a claim employs any formof “include” or “comprise” (e.g., comprises, includes, comprising,including, having, etc.) as a preamble or within a claim recitation ofany kind, it is to be understood that additional elements, terms, etc.may be present without falling outside the scope of the correspondingclaim or recitation. As used herein, when the phrase “at least” is usedas the transition term in, for example, a preamble of a claim, it isopen-ended in the same manner as the term “comprising” and “including”are open ended. The term “and/or” when used, for example, in a form suchas A, B, and/or C refers to any combination or subset of A, B, C such as(1) A alone, (2) B alone, (3) C alone, (4) A with B, (5) A with C, (6) Bwith C, and (7) A with B and with C. As used herein in the context ofdescribing structures, components, items, objects and/or things, thephrase “at least one of A and B” is intended to refer to implementationsincluding any of (1) at least one A, (2) at least one B, and (3) atleast one A and at least one B. Similarly, as used herein in the contextof describing structures, components, items, objects and/or things, thephrase “at least one of A or B” is intended to refer to implementationsincluding any of (1) at least one A, (2) at least one B, and (3) atleast one A and at least one B. As used herein in the context ofdescribing the performance or execution of processes, instructions,actions, activities and/or steps, the phrase “at least one of A and B”is intended to refer to implementations including any of (1) at leastone A, (2) at least one B, and (3) at least one A and at least one B.Similarly, as used herein in the context of describing the performanceor execution of processes, instructions, actions, activities and/orsteps, the phrase “at least one of A or B” is intended to refer toimplementations including any of (1) at least one A, (2) at least one B,and (3) at least one A and at least one B.

The program 400 of FIG. 4 includes block 402, in which the example dataretriever 108 retrieves a stream or stream data from a process (e.g.,retrieved from a control system 102 in a manufacturing environment). Theexample metadata extractor 110 parses the retrieved stream for processdata and extracts the same (block 404). The example metadata extractor110 also determines whether the retrieved stream is a new process thathas not yet been examined to apply labels (block 406). In the event theretrieved stream has not yet been examined, as determined by an exampleflag indicating the same, the example labeler 112 applies labels to thedata within the retrieved stream (block 408), such as labels associatedwith equipment, equipment boundaries, etc. In some examples, the labeler112 employs one or more lookup tables stored in the example labelassociation storage 114 to resolve and/or otherwise normalizenomenclature of the retrieved stream. As described above, because aretrieved stream may be evaluated by the example boundary analyzer 106any number of times/iterations, resolved nomenclature (e.g., commonnames for boundaries) may be written to one or more data elements of theretrieved stream for future analysis, thereby bypassing repeatedlabeling efforts for future iterations.

After the example labeler 112 applies labeling efforts (block 408), orif the example metadata extractor 110 determines that labeling effortsare not needed (block 406) (e.g., because such labeling efforts werepreviously performed and applied to the retrieved stream), the examplestream comparer 116 compares the retrieved (current) stream to streamprofiles (block 410). As described above, at least one objective of theexample stream comparer 116 is to generate and/or otherwise identify aset of streams that are similar to the current stream. Once similarstreams have been identified, examples disclosed herein use suchempirical data to calculate boundary vectors and/or other factors toenable modification and/or tailoring of boundary values of the retrievedstream so that it reflects a degree of consistency with known streamsthat operate well in their respective environments.

FIG. 5. illustrates additional detail in connection with example block410 of FIG. 4. In the illustrated example of FIG. 5, the metadataextractor 110 selects a metadata parameter/boundary name from theretrieved stream (block 502). Retrieved streams may have any number ofboundaries (e.g., parameters having associated metadata indicative of aname of the boundary), in which each boundary name has a correspondingvalue (e.g., a setpoint operating condition for control equipment). Theexample candidate stream selector 118 identifies candidate streamprofiles stored in the example stream profile storage 138 (block 504).As described above, candidate stream profiles are identified by theexample candidate stream selector 118 if at least one metadata boundaryname matches a boundary name of the retrieved stream (block 504). Theexample metadata extractor 110 determines whether there are one or moreadditional metadata parameters of the retrieved stream that have not yetbeen analyzed (block 506). If so, control returns to block 502.

Although the retrieved stream may have been evaluated by the examplelabeler 112 to apply known labels to resolve and/or otherwise normalizenomenclature of the stream, another label resolution effort is appliedby the metadata label resolver 120 after candidate stream profiles havebeen identified. For instance, the example stream profile storage 138and/or the example control profile storage 140 may be updated by one ormore concurrent boundary analysis processes, in which newly distributedstreams have been added with alternate parameter nomenclature. To ensuresuch nomenclature is current and/or otherwise resolved in view of theretrieved stream, the example metadata label resolver 120 appliesNatural Language Processing (NLP) techniques and/or lookup tables to theidentified candidate streams (block 508). Matches that are discovered bythe example metadata label resolver 120 are stored as candidate matchingstreams (block 510) to be further evaluated so that proportionalweighting can be applied based on a degree of similarity. Control thenreturns to block 412 of FIG. 4.

Returning to FIG. 4, the example stream weighting engine 122 appliesweighting to the retrieved stream (block 412). FIG. 6 illustratesadditional detail in connection with block 412 of FIG. 4. In theillustrated example of FIG. 6, the example candidate stream selector 118selects one of the matching streams identified by the example candidatestream selector 118 (block 602). The example difference calculator 124calculates a percent difference for each boundary that is common to theselected matching stream and the retrieved stream (block 604). Asdiscussed above in connection with FIG. 3, percent difference values arecalculated and applied to the example stream table of FIG. 2 (seecolumns 230 and 232). The example similarity metric calculator 126calculates a similarity metric based on an aggregate consideration ofhow substantial the percent difference values are for boundaries commonto the selected matching streams and the retrieved stream (block 606).In particular, and as illustrated in the example of FIG. 2, thesimilarity metric S_(4N) (242) is based on the average of individualpercent difference values subtracted from one-hundred to yield a decimalvalue between zero and one.

The example candidate stream selector 118 determines whether there areone or more additional streams for which a similarity metric is to becalculated (block 608). If so, control returns to block 602 in which adifferent matching stream is selected. Otherwise, the example boundaryvector calculator 128 calculates a boundary vector factor for theretrieved stream (block 610). As described above, the example boundaryvector calculator 128 calculates a boundary vector factor for theretrieved stream in a manner consistent with example Equation 2 orexample Equation 3, the later of which is illustrated in FIG. 3. Toillustrate, the example boundary vector calculator 128 calculated theboundary vector factor for the retrieved stream as the value 0.9586 (seeitem 314 of FIG. 3). Using the retrieved stream TEMP-HIGH boundary 206as an example, in which it has a corresponding value of 127 degrees F.,application of the factor causes generation of an updated value of 121.7degrees F. As such, the example control configuration manager 150applies boundary value modifications to create an updated stream, andfurther applies the updated stream to the environment for execution(block 414).

In the illustrated example of FIG. 4, completion of block 414 by theexample control configuration manager 150 can cause the initiation of aruntime behavior for the example control environment 100, in whichcontrol advances to block 418, as described in further detail below. Onthe other hand, the example boundary analyzer 106 may instead proceed toevaluate one or more additional retrieved strings (see dashed arrow416), in which control returns to block 402. In the event the exampleboundary analyzer 106 analyzes a runtime mode of a process, the examplelimit tester 132 determines whether a boundary value (e.g., the updatedboundary value of the retrieved stream) has been exceeded (block 418).If so, the example control configuration manager 150 causes correctiveactions to be initiated, as dictated by a control system of the examplecontrol environment 100 (block 420). On the other hand, if the examplelimit tester 132 determines that the boundary value has not beenexceeded (block 418), then the example control configuration manager 150causes the process to continue with normal operation (block 422).

The example override detector 134 captures the response activity to theboundary value occurrence (e.g., an override decision by a controlengineer, an ignore decision, etc.) (block 424), and provides thatoccurrence information to the example artificial intelligence engine 136(block 426). The example artificial intelligence engine 136 updates theboundary values based on one or more identified trends and/or patternsof behavior (block 428), such as repeated override decisions by controlengineers. For instance, repeated override decisions/behaviors areindicative of a boundary value that is not set correctly, therebycausing process interruption and/or process inefficiency.

The example stream comparer 116 determines whether an iteration countfor the retrieved stream satisfies (e.g., is greater than) a thresholdvalue (block 430). As described above, the retrieved stream (which mayhave been updated in view of (a) adjustments based on example Equation 2or example Equation 3, and/or (b) artificial intelligence analysis) maybe associated with a relatively new control configuration. As such, theretrieved stream may not be properly vetted until a threshold number ofiterations through the example program 400 of FIG. 4. In the event theexample stream comparer 116 determines that the threshold is notsatisfied (block 430), then control returns to block 402. Otherwise, theexample stream comparer 116 flags the retrieved stream and/or streamboundary values for distribution (block 432). Stated differently, thosestreams and/or stream boundary values that are flagged for distributionmay be used as guides for other control systems having similar controlequipment and/or control objectives.

As described above, examples disclosed herein assist control personnelwhen initially configuring a new manufacturing and/or other controlenvironment that has not previously been established. FIG. 7 illustratesan example program 700 to assist new control environment configuration.In the illustrated example of FIG. 7, the example control configurationmanager 150 determines whether a request has occurred to configure a newenvironment (block 702). If so, the example environment detail extractor130 retrieves environment details (block 704). As described above,environment details include, but are not limited to a list of controlequipment, manufacturer names of the control equipment, model/serialnumbers of the control equipment, nomenclature designating an intendedpurpose of the control equipment (e.g., air tool supply, condensatecollector, etc.), etc. The example environment detail extractor 130selects a match between the environment detail and one or more controlprofiles stored in the example control profile storage 140 that havepreviously been flagged for distribution (block 706). Boundary valuesfrom matching control profiles are used by the example controlconfiguration manager 150 to configure the new control system and/orequipment therein with boundary values (block 708). The example controlconfiguration manager 150 then enables a runtime mode for the newlyestablished control system (block 710).

FIG. 8 is a block diagram of an example processor platform 800structured to execute the instructions of FIGS. 4-7 to implement theexample boundary analyzer 106 of FIG. 1. The processor platform 800 canbe, for example, a server, a personal computer, a workstation, aself-learning machine (e.g., a neural network), a mobile device (e.g., acell phone, a smart phone, a tablet such as an iPad™), a personaldigital assistant (PDA), an Internet appliance, a gaming console, a settop box, a wearable device, or any other type of computing device.

The processor platform 800 of the illustrated example includes aprocessor 812. The processor 812 of the illustrated example is hardware.For example, the processor 812 can be implemented by one or moreintegrated circuits, logic circuits, microprocessors, GPUs, DSPs, orcontrollers from any desired family or manufacturer. The hardwareprocessor may be a semiconductor based (e.g., silicon based) device. Inthis example, the processor implements the example data retriever 108,the example metadata extractor 110, the example labeler, the examplestream comparer 116, the example candidate stream selector 118, theexample metadata label resolver 120, the example stream weighting engine122, the example difference calculator 124, the example similaritymetric calculator 126, the example boundary vector calculator 128, theexample control configuration manager 150, the example environmentdetail extractor 130, the example limit tester 132, the example overridedetector 134, the example artificial intelligence engine 136 and/or,more generally, the example boundary analyzer 106.

The processor 812 of the illustrated example includes a local memory 813(e.g., a cache). The processor 812 of the illustrated example is incommunication with a main memory including a volatile memory 814 and anon-volatile memory 816 via a bus 818. The volatile memory 814 may beimplemented by Synchronous Dynamic Random Access Memory (SDRAM), DynamicRandom Access Memory (DRAM), RAMBUS® Dynamic Random Access Memory(RDRAM®) and/or any other type of random access memory device. Thenon-volatile memory 816 may be implemented by flash memory and/or anyother desired type of memory device. Access to the main memory 814, 816is controlled by a memory controller.

The processor platform 800 of the illustrated example also includes aninterface circuit 820. The interface circuit 820 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), a Bluetooth® interface, a near fieldcommunication (NFC) interface, and/or a PCI express interface.

In the illustrated example, one or more input devices 822 are connectedto the interface circuit 820. The input device(s) 822 permit(s) a userto enter data and/or commands into the processor 812. The inputdevice(s) can be implemented by, for example, an audio sensor, amicrophone, a camera (still or video), a keyboard, a button, a mouse, atouchscreen, a track-pad, a trackball, isopoint and/or a voicerecognition system.

One or more output devices 824 are also connected to the interfacecircuit 820 of the illustrated example. The output devices 824 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay (LCD), a cathode ray tube display (CRT), an in-place switching(IPS) display, a touchscreen, etc.), a tactile output device, a printerand/or speaker. The interface circuit 820 of the illustrated example,thus, typically includes a graphics driver card, a graphics driver chipand/or a graphics driver processor.

The interface circuit 820 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem, a residential gateway, a wireless access point, and/or a networkinterface to facilitate exchange of data with external machines (e.g.,computing devices of any kind) via a network 826. The communication canbe via, for example, an Ethernet connection, a digital subscriber line(DSL) connection, a telephone line connection, a coaxial cable system, asatellite system, a line-of-site wireless system, a cellular telephonesystem, etc.

The processor platform 800 of the illustrated example also includes oneor more mass storage devices 828 for storing software and/or data.Examples of such mass storage devices 828 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, redundantarray of independent disks (RAID) systems, and digital versatile disk(DVD) drives.

The machine executable instructions 832 of FIGS. 4-7 may be stored inthe mass storage device 828, in the volatile memory 814, in thenon-volatile memory 816, and/or on a removable non-transitory computerreadable storage medium such as a CD or DVD.

From the foregoing, it will be appreciated that example methods,apparatus and articles of manufacture have been disclosed that improve atechnical effect of process control execution by, at least, reducing afrequency of nuisance excursions that are caused by inappropriatelyconfigured setpoints. Examples disclosed herein also improve processsafety by tailoring boundaries that may have been set due to mereguesswork, heuristics and/or ignorance of control personnel charteredwith the responsibility to configure manufacturing environment equipmentsetpoints. The disclosed methods, apparatus and articles of manufactureimprove the efficiency of using a computing device by reducing afrequency of process execution interruptions, in which such processdowntime typically results in wasted energy consumption and/or idlepersonnel resources that await process re-initialization after one ormore nuisance excursions. The disclosed methods, apparatus and articlesof manufacture are accordingly directed to one or more improvement(s) inthe functioning of a computer.

Example 1 includes an apparatus to improve boundary excursion detection,the apparatus comprising a metadata extractor to parse a first controlstream to extract embedded metadata, a metadata label resolver toclassify a boundary term of the extracted embedded metadata, a candidatestream selector to identify candidate second control streams thatinclude a boundary term that matches the classified boundary term of thefirst control stream, and a boundary vector calculator to improveboundary excursion detection by calculating a boundary vector factorbased on respective ones of the candidate second control streams thatinclude the classified boundary term.

Example 2 includes the apparatus as defined in example 1, furtherincluding a control configuration manager to generate an updated controlstream by applying the boundary vector factor to boundary valuesassociated with respective boundary terms of the first control stream.

Example 3 includes the apparatus as defined in example 2, wherein theupdated control stream includes updated boundary values to reduce afrequency of nuisance boundary excursions.

Example 4 includes the apparatus as defined in example 1, furtherincluding a difference calculator to calculate a difference valuebetween (a) a boundary value associated with a respective boundary termof the first control stream and (b) a boundary value associated with amatching boundary term of the second control stream.

Example 5 includes the apparatus as defined in example 4, furtherincluding a similarity metric calculator to calculate a similaritymetric based on an average of a plurality of ones of the differencevalue.

Example 6 includes the apparatus as defined in example 5, wherein thesimilarity metric is indicative of a magnitude of similarity between thefirst control stream and the second control stream.

Example 7 includes the apparatus as defined in example 5, furtherincluding a boundary vector calculator to proportionally adjust theboundary value associated with the boundary term of the first controlstream based on the similarity metric.

Example 8 includes the apparatus as defined in example 1, furtherincluding an override detector to capture post excursion inputs.

Example 9 includes the apparatus as defined in example 8, wherein thepost excursion inputs include at least one of an override instruction,an acceptance instruction, a process shut-down instruction, or amaintenance request instruction.

Example 10 includes the apparatus as defined in example 8, wherein theoverride detector is to forward the post excursion inputs to anartificial intelligence (ai) engine, the ai engine to identify candidateboundary value adjustments based on the post excursion inputs.

Example 11 includes a non-transitory computer readable medium comprisinginstructions that, when executed, cause one or more processor to, atleast parse a first control stream to extract embedded metadata,classify a boundary term of the extracted embedded metadata, identifycandidate second control streams that include a boundary term thatmatches the classified boundary term of the first control stream, andimprove boundary excursion detection by calculating a boundary vectorfactor based on respective ones of the candidate second control streamsthat include the classified boundary term.

Example 12 includes the non-transitory computer readable medium asdefined in example 11, wherein the instructions, when executed, causethe at least one processor to generate an updated control stream byapplying the boundary vector factor to boundary values associated withrespective boundary terms of the first control stream.

Example 13 includes the non-transitory computer readable medium asdefined in example 12, wherein the instructions, when executed, causethe at least one processor to reduce a frequency of nuisance boundaryexcursions by applying updated boundary values.

Example 14 includes the non-transitory computer readable medium asdefined in example 11, wherein the instructions, when executed, causethe at least one processor to calculate a difference value between (a) aboundary value associated with a respective boundary term of the firstcontrol stream and (b) a boundary value associated with a matchingboundary term of the second control stream.

Example 15 includes the non-transitory computer readable medium asdefined in example 14, wherein the instructions, when executed, causethe at least one processor to calculate a similarity metric based on anaverage of a plurality of ones of the difference value.

Example 16 includes the non-transitory computer readable medium asdefined in example 15, wherein the instructions, when executed, causethe at least one processor to proportionally adjust the boundary valueassociated with the boundary term of the first control stream based onthe similarity metric.

Example 17 includes the non-transitory computer readable medium asdefined in example 11, wherein the instructions, when executed, causethe at least one processor to capture post excursion inputs.

Example 18 includes the non-transitory computer readable medium asdefined in example 17, wherein the instructions, when executed, causethe at least one processor to identify at least one of an overrideinstruction, an acceptance instruction, a process shut-down instruction,or a maintenance request instruction.

Example 19 includes the non-transitory computer readable medium asdefined in example 17, wherein the instructions, when executed, causethe at least one processor to forward the post excursion inputs to anartificial intelligence (ai) engine, the ai engine to identify candidateboundary value adjustments based on the post excursion inputs.

Example 20 includes a computer-implemented method to improve boundaryexcursion detection, the method comprising parsing, by executing aninstruction with at least one processor, a first control stream toextract embedded metadata, classifying, by executing an instruction withthe at least one processor, a boundary term of the extracted embeddedmetadata, identifying, by executing an instruction with the at least oneprocessor, candidate second control streams that include a boundary termthat matches the classified boundary term of the first control stream,and improving, by executing an instruction with the at least oneprocessor, boundary excursion detection by calculating a boundary vectorfactor based on respective ones of the candidate second control streamsthat include the classified boundary term.

Example 21 includes the computer-implemented method as defined inexample 20, further including generating an updated control stream byapplying the boundary vector factor to boundary values associated withrespective boundary terms of the first control stream.

Example 22 includes the computer-implemented method as defined inexample 21, further including applying updated boundary values in theupdated control stream to reduce a frequency of nuisance boundaryexcursions.

Example 23 includes the computer-implemented method as defined inexample 20, further including calculating a difference value between (a)a boundary value associated with a respective boundary term of the firstcontrol stream and (b) a boundary value associated with a matchingboundary term of the second control stream.

Example 24 includes the computer-implemented method as defined inexample 23, further including calculating a similarity metric based onan average of a plurality of ones of the difference value.

Example 25 includes the computer-implemented method as defined inexample 24, wherein the similarity metric is indicative of a magnitudeof similarity between the first control stream and the second controlstream.

Example 26 includes the computer-implemented method as defined inexample 24, further including proportionally adjusting the boundaryvalue associated with the boundary term of the first control streambased on the similarity metric.

Example 27 includes the computer-implemented method as defined inexample 20, further including capturing post excursion inputs.

Example 28 includes the computer-implemented method as defined inexample 27, wherein the post excursion inputs include at least one of anoverride instruction, an acceptance instruction, a process shut-downinstruction, or a maintenance request instruction.

Example 29 includes the computer-implemented method as defined inexample 27, further including forwarding the post excursion inputs to anartificial intelligence (ai) engine, the ai engine to identify candidateboundary value adjustments based on the post excursion inputs.

Example 30 includes a system for improving boundary excursion detection,the system comprising means for parsing a first control stream toextract embedded metadata, means for classifying a boundary term of theextracted embedded metadata, means for identifying candidate secondcontrol streams that include a boundary term that matches the classifiedboundary term of the first control stream, and means for calculating aboundary vector factor based on respective ones of the candidate secondcontrol streams that include the classified boundary term.

Example 31 includes the system as defined in example 30, furtherincluding means for generating an updated control stream by applying theboundary vector factor to boundary values associated with respectiveboundary terms of the first control stream.

Example 32 includes the system as defined in example 31, wherein theupdated control stream includes updated boundary values to reduce afrequency of nuisance boundary excursions.

Example 33 includes the system as defined in example 30, furtherincluding means for calculating a difference value between (a) aboundary value associated with a respective boundary term of the firstcontrol stream and (b) a boundary value associated with a matchingboundary term of the second control stream.

Example 34 includes the system as defined in example 33, furtherincluding means for calculating a similarity metric based on an averageof a plurality of ones of the difference value.

Example 35 includes the system as defined in example 34, wherein thesimilarity metric is indicative of a magnitude of similarity between thefirst control stream and the second control stream.

Example 36 includes the system as defined in example 34, furtherincluding means for adjusting the boundary value associated with theboundary term of the first control stream based on the similaritymetric.

Example 37 includes the system as defined in example 30, furtherincluding means for capturing post excursion inputs.

Example 38 includes the system as defined in example 37, wherein thepost excursion inputs include at least one of an override instruction,an acceptance instruction, a process shut-down instruction, or amaintenance request instruction.

Example 39 includes the system as defined in example 37, wherein thecapturing means is to forward the post excursion inputs to an artificialintelligence (ai) engine, the ai engine to identify candidate boundaryvalue adjustments based on the post excursion inputs.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

1. An apparatus to improve boundary excursion detection, the apparatuscomprising: a metadata extractor to parse a first control stream toextract embedded metadata; a metadata label resolver to classify aboundary term of the extracted embedded metadata; a candidate streamselector to identify candidate second control streams that include aboundary term that matches the classified boundary term of the firstcontrol stream; and a boundary vector calculator to improve boundaryexcursion detection by calculating a boundary vector factor based onrespective ones of the candidate second control streams that include theclassified boundary term.
 2. The apparatus as defined in claim 1,further including a control configuration manager to generate an updatedcontrol stream by applying the boundary vector factor to boundary valuesassociated with respective boundary terms of the first control stream.3. The apparatus as defined in claim 2, wherein the updated controlstream includes updated boundary values to reduce a frequency ofnuisance boundary excursions.
 4. The apparatus as defined in claim 1,further including a difference calculator to calculate a differencevalue between (a) a boundary value associated with a respective boundaryterm of the first control stream and (b) a boundary value associatedwith a matching boundary term of the second control stream.
 5. Theapparatus as defined in claim 4, further including a similarity metriccalculator to calculate a similarity metric based on an average of aplurality of ones of the difference value.
 6. The apparatus as definedin claim 5, wherein the similarity metric is indicative of a magnitudeof similarity between the first control stream and the second controlstream.
 7. The apparatus as defined in claim 5, further including aboundary vector calculator to proportionally adjust the boundary valueassociated with the boundary term of the first control stream based onthe similarity metric.
 8. The apparatus as defined in claim 1, furtherincluding an override detector to capture post excursion inputs.
 9. Theapparatus as defined in claim 8, wherein the post excursion inputsinclude at least one of an override instruction, an acceptanceinstruction, a process shut-down instruction, or a maintenance requestinstruction.
 10. The apparatus as defined in claim 8, wherein theoverride detector is to forward the post excursion inputs to anartificial intelligence (AI) engine, the AI engine to identify candidateboundary value adjustments based on the post excursion inputs.
 11. Anon-transitory computer readable medium comprising instructions that,when executed, cause one or more processor to, at least: parse a firstcontrol stream to extract embedded metadata; classify a boundary term ofthe extracted embedded metadata; identify candidate second controlstreams that include a boundary term that matches the classifiedboundary term of the first control stream; and improve boundaryexcursion detection by calculating a boundary vector factor based onrespective ones of the candidate second control streams that include theclassified boundary term.
 12. The non-transitory computer readablemedium as defined in claim 11, wherein the instructions, when executed,cause the at least one processor to generate an updated control streamby applying the boundary vector factor to boundary values associatedwith respective boundary terms of the first control stream.
 13. Thenon-transitory computer readable medium as defined in claim 12, whereinthe instructions, when executed, cause the at least one processor toreduce a frequency of nuisance boundary excursions by applying updatedboundary values.
 14. The non-transitory computer readable medium asdefined in claim 11, wherein the instructions, when executed, cause theat least one processor to calculate a difference value between (a) aboundary value associated with a respective boundary term of the firstcontrol stream and (b) a boundary value associated with a matchingboundary term of the second control stream.
 15. The non-transitorycomputer readable medium as defined in claim 14, wherein theinstructions, when executed, cause the at least one processor tocalculate a similarity metric based on an average of a plurality of onesof the difference value.
 16. The non-transitory computer readable mediumas defined in claim 15, wherein the instructions, when executed, causethe at least one processor to proportionally adjust the boundary valueassociated with the boundary term of the first control stream based onthe similarity metric.
 17. The non-transitory computer readable mediumas defined in claim 11, wherein the instructions, when executed, causethe at least one processor to capture post excursion inputs.
 18. Thenon-transitory computer readable medium as defined in claim 17, whereinthe instructions, when executed, cause the at least one processor toidentify at least one of an override instruction, an acceptanceinstruction, a process shut-down instruction, or a maintenance requestinstruction.
 19. The non-transitory computer readable medium as definedin claim 17, wherein the instructions, when executed, cause the at leastone processor to forward the post excursion inputs to an artificialintelligence (AI) engine, the AI engine to identify candidate boundaryvalue adjustments based on the post excursion inputs.
 20. Acomputer-implemented method to improve boundary excursion detection, themethod comprising: parsing, by executing an instruction with at leastone processor, a first control stream to extract embedded metadata;classifying, by executing an instruction with the at least oneprocessor, a boundary term of the extracted embedded metadata;identifying, by executing an instruction with the at least oneprocessor, candidate second control streams that include a boundary termthat matches the classified boundary term of the first control stream;and improving, by executing an instruction with the at least oneprocessor, boundary excursion detection by calculating a boundary vectorfactor based on respective ones of the candidate second control streamsthat include the classified boundary term.
 21. The computer-implementedmethod as defined in claim 20, further including generating an updatedcontrol stream by applying the boundary vector factor to boundary valuesassociated with respective boundary terms of the first control stream.22. The computer-implemented method as defined in claim 21, furtherincluding applying updated boundary values in the updated control streamto reduce a frequency of nuisance boundary excursions.
 23. Thecomputer-implemented method as defined in claim 20, further includingcalculating a difference value between (a) a boundary value associatedwith a respective boundary term of the first control stream and (b) aboundary value associated with a matching boundary term of the secondcontrol stream.
 24. The computer-implemented method as defined in claim23, further including calculating a similarity metric based on anaverage of a plurality of ones of the difference value.
 25. Thecomputer-implemented method as defined in claim 24, wherein thesimilarity metric is indicative of a magnitude of similarity between thefirst control stream and the second control stream.
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